Describe: education
This report lists the candidate variable for DataScheme variables of the construct education.
Exposition
This report is meant to be compiled after having executed the script
./manipulation/0-ellis-island.R, which prepares the necessary data transfer object (DTO). We begin with a brief recap of this script and the DTO it produces.
Ellis Island
All data land on Ellis Island.
The script 0-ellis-island.R is the first script in the analytic workflow. It accomplished the following:
- Reads in raw data files from the candidate studies
- Reads in raw data files from the candidate studies
- Extract, combines, and exports their metadata (specifically, variable names and labels, if provided) into
./data/shared/derived/meta-data-live.csv, which is updated every time Ellis Island script is executed.
- Extract, combines, and exports their metadata (specifically, variable names and labels, if provided) into
- Augments raw metadata with instructions for renaming and classifying variables. The instructions are provided as manually entered values in
./data/shared/meta-data-map.csv. They are used by automatic scripts in later harmonization and analysis.
- Augments raw metadata with instructions for renaming and classifying variables. The instructions are provided as manually entered values in
- Combines unit and metadata into a single DTO to serve as a starting point to all subsequent analyses.
# load the product of 0-ellis-island.R, a list object containing data and metadata
dto <- readRDS("./data/unshared/derived/dto.rds")# the list is composed of the following elements
names(dto)[1] "studyName" "filePath" "unitData" "metaData" # 1st element - names of the studies as character vector
dto[["studyName"]][1] "alsa" "lbsl" "satsa" "share" "tilda"# 2nd element - file paths of the data files for each study as character vector
dto[["filePath"]][1] "./data/unshared/raw/ALSA-Wave1.Final.sav" "./data/unshared/raw/LBSL-Panel2-Wave1.Final.sav"
[3] "./data/unshared/raw/SATSA-Q3.Final.sav" "./data/unshared/raw/SHARE-Israel-Wave1.Final.sav"
[5] "./data/unshared/raw/TILDA-Wave1.Final.sav" # 3rd element - list objects with the following elements
names(dto[["unitData"]])[1] "alsa" "lbsl" "satsa" "share" "tilda"# each of these elements is a raw data set of a corresponding study, for example
dplyr::tbl_df(dto[["unitData"]][["lbsl"]]) Source: local data frame [656 x 27]
id AGE94 SEX94 MSTAT94 EDUC94 NOWRK94 SMK94 SMOKE
(int) (int) (int) (fctr) (int) (fctr) (fctr) (fctr)
1 4001026 68 1 divorced 16 no, retired no never smoked
2 4012015 94 2 widowed 12 no, retired no never smoked
3 4012032 94 2 widowed 20 no, retired no don't smoke at present but smoked in the past
4 4022004 93 2 NA NA NA NA never smoked
5 4022026 93 2 widowed 12 no, retired no never smoked
6 4031031 92 1 married 8 no, retired no don't smoke at present but smoked in the past
7 4031035 92 1 widowed 13 no, retired no don't smoke at present but smoked in the past
8 4032201 92 2 NA NA NA NA don't smoke at present but smoked in the past
9 4041062 91 1 widowed 7 NA no don't smoke at present but smoked in the past
10 4042057 91 2 NA NA NA NA NA
.. ... ... ... ... ... ... ... ...
Variables not shown: ALCOHOL (fctr), WINE (int), BEER (int), HARDLIQ (int), SPORT94 (int), FIT94 (int), WALK94 (int),
SPEC94 (int), DANCE94 (int), CHORE94 (int), EXCERTOT (int), EXCERWK (int), HEIGHT94 (int), WEIGHT94 (int), HWEIGHT
(int), HHEIGHT (int), SRHEALTH (fctr), smoke_now (lgl), smoked_ever (lgl)Meta
# 4th element - a dataset names and labels of raw variables + added metadata for all studies
dto[["metaData"]] %>% dplyr::select(study_name, name, item, construct, type, categories, label_short, label) %>%
DT::datatable(
class = 'cell-border stripe',
caption = "This is the primary metadata file. Edit at `./data/shared/meta-data-map.csv",
filter = "top",
options = list(pageLength = 6, autoWidth = TRUE)
)ALSA
SCHOOL
dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="SCHOOL") %>% dplyr::select(name,label) name label
1 SCHOOL Age left schooldto[["unitData"]][["alsa"]]%>% histogram_discrete("SCHOOL")
dto[["unitData"]][["alsa"]]%>% dplyr::group_by_("SCHOOL") %>% dplyr::summarize(n=n())Source: local data frame [8 x 2]
SCHOOL n
(fctr) (int)
1 Never went to school 30
2 Under fourteen years 306
3 Fourteen years 819
4 Fifteen years 382
5 Sixteen years 280
6 Seventeen years 131
7 Eighteen or more years 113
8 NA 26TYPQUAL
dto[["metaData"]] %>% dplyr::filter(study_name=="alsa", name=="TYPQUAL") %>% dplyr::select(name,label) name label
1 TYPQUAL Highest qualificationdto[["unitData"]][["alsa"]]%>% histogram_discrete("TYPQUAL")
dto[["unitData"]][["alsa"]]%>% dplyr::group_by_("TYPQUAL") %>% dplyr::summarize(n=n())Source: local data frame [10 x 2]
TYPQUAL n
(fctr) (int)
1 Primary School Course 1
2 Secondary School Course 17
3 Trade or Apprenticeship 236
4 Certificate or Diploma 332
5 Bachelor Degree or Post Graduate Diploma 80
6 Higher Qualification 14
7 Adult Education or Hobby Course 11
8 Other 6
9 No Formal Tuition 3
10 NA 1387LBSL
EDUC94
dto[["metaData"]] %>% dplyr::filter(study_name=="lbsl", name=="EDUC94") %>% dplyr::select(name,label) name label
1 EDUC94 Number of Years of school completed (1-20)dto[["unitData"]][["lbsl"]]%>% histogram_discrete("EDUC94")
dto[["unitData"]][["lbsl"]]%>% dplyr::group_by_("EDUC94") %>% dplyr::summarize(n=n())Source: local data frame [18 x 2]
EDUC94 n
(int) (int)
1 4 1
2 7 6
3 8 16
4 9 4
5 10 29
6 11 18
7 12 170
8 13 40
9 14 85
10 15 37
11 16 62
12 17 15
13 18 28
14 19 10
15 20 31
16 21 1
17 23 1
18 NA 102SATSA
EDUC
dto[["metaData"]] %>% dplyr::filter(study_name=="satsa", name=="EDUC") %>% dplyr::select(name,label) name label
1 EDUC Educationdto[["unitData"]][["satsa"]]%>% histogram_discrete("EDUC")
dto[["unitData"]][["satsa"]]%>% dplyr::group_by_("EDUC") %>% dplyr::summarize(n=n())Source: local data frame [5 x 2]
EDUC n
(fctr) (int)
1 Elementary school 858
2 O-level or vocational school or folk school 381
3 gymnasium (A-level) 121
4 university or higher 109
5 NA 28SHARE
DN012D01
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN012D01") %>% dplyr::select(name,label) name label
1 DN012D01 yeshiva, religious high institutiondto[["unitData"]][["share"]]%>% histogram_discrete("DN012D01")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN012D01") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
DN012D01 n
(fctr) (int)
1 not selected 2581
2 selected 16
3 NA 1DN012D02
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN012D02") %>% dplyr::select(name,label) name label
1 DN012D02 nursing schooldto[["unitData"]][["share"]]%>% histogram_discrete("DN012D02")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN012D02") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
DN012D02 n
(fctr) (int)
1 not selected 2543
2 selected 54
3 NA 1DN012D03
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN012D03") %>% dplyr::select(name,label) name label
1 DN012D03 polytechnicdto[["unitData"]][["share"]]%>% histogram_discrete("DN012D03")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN012D03") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
DN012D03 n
(fctr) (int)
1 not selected 2482
2 selected 115
3 NA 1DN012D04
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN012D04") %>% dplyr::select(name,label) name label
1 DN012D04 university, Bachelors degreedto[["unitData"]][["share"]]%>% histogram_discrete("DN012D04")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN012D04") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
DN012D04 n
(fctr) (int)
1 not selected 2213
2 selected 384
3 NA 1DN012D05
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN012D05") %>% dplyr::select(name,label) name label
1 DN012D05 university, graduate degreedto[["unitData"]][["share"]]%>% histogram_discrete("DN012D05")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN012D05") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
DN012D05 n
(fctr) (int)
1 not selected 2358
2 selected 239
3 NA 1DN012D09
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN012D09") %>% dplyr::select(name,label) name label
1 DN012D09 still in further education or trainingdto[["unitData"]][["share"]]%>% histogram_discrete("DN012D09")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN012D09") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
DN012D09 n
(fctr) (int)
1 not selected 2588
2 selected 9
3 NA 1DN012DNO
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN012DNO") %>% dplyr::select(name,label) name label
1 DN012DNO no further educationdto[["unitData"]][["share"]]%>% histogram_discrete("DN012DNO")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN012DNO") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
DN012DNO n
(fctr) (int)
1 not selected 1077
2 selected 1520
3 NA 1DN012DOT
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN012DOT") %>% dplyr::select(name,label) name label
1 DN012DOT other further educationdto[["unitData"]][["share"]]%>% histogram_discrete("DN012DOT")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN012DOT") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
DN012DOT n
(fctr) (int)
1 not selected 2320
2 selected 277
3 NA 1DN012DRF
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN012DRF") %>% dplyr::select(name,label) name label
1 DN012DRF refuseddto[["unitData"]][["share"]]%>% histogram_discrete("DN012DRF")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN012DRF") %>% dplyr::summarize(n=n())Source: local data frame [2 x 2]
DN012DRF n
(fctr) (int)
1 not selected 2597
2 NA 1DN012DDK
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN012DDK") %>% dplyr::select(name,label) name label
1 DN012DDK dont knowdto[["unitData"]][["share"]]%>% histogram_discrete("DN012DDK")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN012DDK") %>% dplyr::summarize(n=n())Source: local data frame [3 x 2]
DN012DDK n
(fctr) (int)
1 not selected 2596
2 selected 1
3 NA 1DN0100
dto[["metaData"]] %>% dplyr::filter(study_name=="share", name=="DN0100") %>% dplyr::select(name,label) name label
1 DN0100 highest educational degree obtaineddto[["unitData"]][["share"]]%>% histogram_discrete("DN0100")
dto[["unitData"]][["share"]]%>% dplyr::group_by_("DN0100") %>% dplyr::summarize(n=n())Source: local data frame [13 x 2]
DN0100 n
(fctr) (int)
1 Elementary school 501
2 Partial occipational secondary school (did not graduate, no 102
3 full occipational secondary school (no matriculation) 174
4 full occipational secondary school (with matriculation) 113
5 partial Academic secondary school (no matriculation) 219
6 full Academic secondary school (no matriculation) 274
7 full Academic secondary school (with matriculation) 1024
8 yeshiva secondary school (no matriculation) 8
9 yeshiva secondary school (wih matriculation) 6
10 none 143
11 other type (also abroad) 32
12 don't know 1
13 NA 1TILDA
DM001
dto[["metaData"]] %>% dplyr::filter(study_name=="tilda", name=="DM001") %>% dplyr::select(name,label) name label
1 DM001 dm001 What is the highest level of education you have completeddto[["unitData"]][["tilda"]]%>% histogram_discrete("DM001")
dto[["unitData"]][["tilda"]]%>% dplyr::group_by_("DM001") %>% dplyr::summarize(n=n())Source: local data frame [9 x 2]
DM001 n
(fctr) (int)
1 Some primary (not complete) 280
2 Primary or equivalent 2232
3 Intermediate/junior/group certificate or equivalent 1971
4 Leaving certificate or equivalent 1460
5 Diploma/certificate 1335
6 Primary degree 730
7 Postgraduate/higher degree 483
8 None 9
9 NA 4sessionInfo()R version 3.2.5 (2016-04-14)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_2.1.0 knitr_1.12.3 magrittr_1.5
loaded via a namespace (and not attached):
[1] splines_3.2.5 lattice_0.20-33 colorspace_1.2-6 htmltools_0.3.5 mgcv_1.8-12
[6] yaml_2.1.13 chron_2.3-47 survival_2.38-3 nloptr_1.0.4 foreign_0.8-66
[11] DBI_0.4-1 RColorBrewer_1.1-2 plyr_1.8.3 stringr_1.0.0 MatrixModels_0.4-1
[16] munsell_0.4.3 gtable_0.2.0 htmlwidgets_0.6 evaluate_0.9 labeling_0.3
[21] latticeExtra_0.6-28 SparseM_1.7 extrafont_0.17 quantreg_5.21 pbkrtest_0.4-6
[26] parallel_3.2.5 markdown_0.7.7 highr_0.5.1 Rttf2pt1_1.3.3 Rcpp_0.12.5
[31] acepack_1.3-3.3 scales_0.4.0 DT_0.1.40 formatR_1.3 Hmisc_3.17-4
[36] jsonlite_0.9.20 lme4_1.1-12 gridExtra_2.2.1 testit_0.5 digest_0.6.9
[41] stringi_1.0-1 dplyr_0.4.3 grid_3.2.5 tools_3.2.5 lazyeval_0.1.10
[46] dichromat_2.0-0 Formula_1.2-1 cluster_2.0.3 tidyr_0.4.1 extrafontdb_1.0
[51] car_2.1-2 MASS_7.3-45 Matrix_1.2-4 rsconnect_0.4.2.1 data.table_1.9.6
[56] assertthat_0.1 minqa_1.2.4 rmarkdown_0.9.6 R6_2.1.2 rpart_4.1-10
[61] nnet_7.3-12 nlme_3.1-126